A Two-Stage Urine Sediment Detection Method

Urine sediment detection is of great significance as one of the routine testing items. The traditional urine sediment detection method is mainly manual microscopic examination. Thus, it incurs heavy human workload and complicated operation, while it is easy to miss the targets. To alleviate this problem, a two-stage urine sediment detection method is proposed in this paper. More specifically, the segmentation and classification tasks are transformed into object detection tasks, and the feature extraction is performed by Deep Convolutional Neural Networks (DCNN). In our method, HOG+SVM is used as region proposal, and Trimmed MobileNets is used for DCNN refining. The experimental results demonstrate that the proposed method achieves promising performance.